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Investigation of the Induration Mechanism of Red Mud Pellets with Tartaric Acid Additions

MINERAL PROCESSING AND EXTRACTIVE METALLURGY REVIEW 2023年第8期44卷 554-564页

作者： Qin, Yihe Yuan, Run Yue, Pu Li, Chunquan Zhang, Xuewei Li, Na Bai, Lu Wang, Zichen Xia, Yu he, xuwen China Univ Min & Technol Sch Chem & Environm Engn Beijing Peoples R China

herein, tartaric acid (TA) was used as a binder to prepare red mud pellets. The effect of TA on the compressive strength and induration mechanism of the pellets containing TA was analyzed via the changes in crystal structure, functional groups, and valence states of the constituent elements. The induration mechanism of TAPE is likely the chemical reaction between TA and partial calcite along with complexation of TA with minerals containing Ca(II), Al(III), and Fe(III). The density functional theory calculation results revealed that the TA-Ca(II) complexation products in red mud were more stable than those of Al(III) and Fe(III).

关键词： Tartaric acid compression strength density functional theory calculation pellet

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Corrigendum to“Temporal assembly patterns of microbial communities in three parallel bioreactors treating low-concentration coking wastewater with differing carbon source concentrations”[Journal of Environmental Sciences,Volume 137,March 2024,Pages 455-468]

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Journal of Environmental Sciences 2024年第3期36卷 701-702页

作者： Weijia Li Yu Xia Na Li Jie Chang Jing Liu Pei Wang xuwen he School of Chemical and Environmental Engineering China University of Mining and Technology-BeijingBeijing 100083China

The authors regret that the first line of Fig.7 has an extra“toluene”.The correct Fig.7 is provided as below.

关键词： wastewater source carbon

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Multi-scale Feature Fusion Extraction Structure for the Leather Defect Detection Algorithm 6

Multi-scale Feature Fusion Extraction Structure for the Leat...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： he, xuwen Li, Hao Huang, Zihan Xiong, Xiaorui Liu, Yifan Yao, Yuting Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350325485

Detection of surface defects using computer vision is a pivotal technology for achieving intelligent manufacturing. Leather products are one of the most widely traded goods in the world, and automatic identification, localization, and detection of surface defects in leather are indispensable for achieving intelligent manufacturing of leather products. This research paper proposes FG-DETR, a multi-scale feature fusion extraction structure for small target detection algorithms. Given the recent success of deep learning methods in various related fields and that certain defects and flaws in leather pictures in our dataset are relatively small. FG-DETR is based on DETR with some improvements as follows, Initially, DETR's ResNet50 network performs feature extraction on the original image and produces four scales of feature maps. Second, we apply a modified Feature Pyramid Network (FPN) to downsample and upsample the four scales of feature maps, followed by the fusion and output of a single feature map. A Gaussian kernel and output then process the fused feature maps as the final feature maps. Finally, the feature maps are merged with the position encoding information and combined into the Transformer encoder-decoder architecture, resulting in the detection outcomes through the Position-wise Feed-Forward Networks (FFN). In our leather dataset, the FG-DETR model is more effective in detecting small targets than DETR, as our leather images are large, and the original DETR is nearly ineffective for images with relatively small defects. The average detection accuracy of medium and large targets also improved, with a 7.6 percentage point improvement at AP50. The experimental findings demonstrate that the multi-scale feature fusion extraction structure significantly enhances the detection accuracy of DETR. © 2023 IEEE.

关键词： Computer vision

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煤矿矿井水资源化绿色短流程关键技术与装备

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煤炭学报 2024年第2期49卷 958-966页

作者：何绪文王绍州张学伟季晓晴中国矿业大学(北京)化学与环境工程学院北京100083

煤炭是我国重要的工业原料,与我国经济发展关系密切。煤炭开采过程中会产生大量矿井水,是重要的非常规水资源,需妥善处理。然而现有矿井水处理技术普遍存在占地大、流程长、需加药、运行维护复杂和出水水质不稳定等问题,为缩短处理流程... 详细信息

煤炭是我国重要的工业原料,与我国经济发展关系密切。煤炭开采过程中会产生大量矿井水,是重要的非常规水资源,需妥善处理。然而现有矿井水处理技术普遍存在占地大、流程长、需加药、运行维护复杂和出水水质不稳定等问题,为缩短处理流程、提高处理效率和保证出水水质,亟需研发新的关键技术与装备。采用聚瓷膜直滤技术处理矿井水,通过对膜通量、膜比通量、运行压力和循环流量等条件的对比分析,确定最佳运行参数为:运行压力0.2 MPa,循环流量18 m3/h,处理过程无需加药,出水悬浮物质量浓度稳定<1 mg/L,浊度稳定<1 NTU。采用广泛应用的膜堵塞模型来探讨膜污染机制,膜污染分析结果表明,膜污堵主要以滤饼层污堵为主,并伴随中间污堵,具体表现为膜的表面污染和膜孔的堵塞。通过水力反洗及时清除膜表面污染,最佳反洗时间为60 s,通量恢复率为85.12%;通过定时化学清洗清除膜孔污堵,用复配制剂清洗3 h,通量恢复率可达95%以上。工程案例表明,聚瓷膜直滤可直接取代“混凝沉淀-过滤-超滤”(一代三),且已连续稳定运行2 a,满足设计产水量要求,系统回收率均在90%以上,处理后水质达到GB 3838—2002地表水环境质量III类24种标准限值要求,且自动化程度高,可无人值守,实现了矿井水资源化利用绿色短流程、节能高效和智能化新要求,可为该技术大规模推广应用提供技术参考。

关键词：煤矿矿井水聚瓷膜直滤膜污染资源化

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End-To-End Individual Pig Detection Based on Transfer Learning 6

End-To-End Individual Pig Detection Based on Transfer Learni...

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6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Huang, Zihan Li, Hao Xiong, Xiaorui he, xuwen Song, Rui Huang, Mengzhen Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350325485

Nowadays, monitoring animals manually on a large scale is challenging, while external devices can provide more prosperous and accurate information. Therefore, there are already applications incorporating wearable sensors to monitor individual pigs. With continued breakthroughs in computer vision, low-cost visible light cameras can help monitor pigs. CNN-based object detection methods have been applied to individual pig detection, but most rely on hand-designed components and require prior knowledge. Based on previous researchers' progress, we propose using DETR, an end-To-end object detection method, to detect individual pigs. Also, to adapt to our natural pig farm environment, we trained the model with transfer learning to achieve an AP of 0.995 on our collected dataset. © 2023 IEEE.

关键词： Object detection

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Removal of viscous and clogging suspended solids in the wastewater from acrylonitrile-butadiene-styrene resin production by a new dissolved air release device

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 148卷 524-535页

作者： Zhang, Xiaohang Zhang, Xuewei Liu, Yanping Zhang, Qing Yang, Shipeng he, xuwen Chinese Acad Sci Ctr Agr Resources Res Inst Genet & Dev Biol Key Lab Agr Water Resources 286 Huaizhong Rd Shijiazhuang 050021 Hebei Peoples R China China Univ Min & Technol Beijing Sch Chem & Environm Engn Xueyuan Rd Beijing 100083 Peoples R China Handan Municipal Publ Util Investment Grp Co Ltd Dept Engn & Technol 58 Guoyuan Rd Handan City 056000 Hebei Peoples R China Tsinghua Univ State Key Joint Lab Environm Simulat & Pollut Con 30 Shuangqing Rd Beijing 100084 Peoples R China

The suspended solids in the acrylonitrile-butadiene-styrene(ABS) wastewater are in the form of viscouslatex, which strengthens the blocking phenomenon of the release device, increases the suspended solids in the return water and reduces the removal effect of the air flotation apparatus. Therefore, we designed a new release device. To achieve the goal of controlling the bubbles in the target diameter and enlarge the hole size as much as possible to reduce blockage, we studied the relationship between the geometry structure and performance of the new, which could be describeed by fixed polynomials. The best demulsification effect could be obtained by the hybrid of the polyferric sulfate and FO4440SSH, and the highest removal rate was 92.51 %. After a long-running in the ABS sewage treatment plant of the petrochemical industry, the removal rate of the new could maintain at a high level(>90 %), and the removal effect was stable. Compared with other release devices that reported in the literatures, the new one had advantages significant in both removal efficiency and operation cost. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Acrylonitrile-butadiene-styrene wastewater Dissolved air flotation The volume of gas release The flow rate Demulsification

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Pilot-scale catalytic ozonation pretreatment for improving the biodegradability of fixed-bed coal gasification wastewater

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 148卷 13-19页

作者： Chen, Xiaoya Wang, Chunrong Jiang, Longxin Li, Haiyan Wang, Jianbing he, xuwen China Univ Min & Technol Beijing Sch Chem & Environm Engn Beijing 100083 Peoples R China

Achieving zero liquid dischargeis a requirement for the development of the coal chemical industry;high pollutant concentration, strong biotoxicity, and poor biodegradability (BOD5/COD=0.1-0.2) make fixed-bed coal gasification wastewater difficult to treat. This study synthesized a Fe-Mn/lava catalyst (FMLC) and performed a pilot-scale test on catalytic ozonation for the pretreatment of fixed-bed coal gasification wastewater. The effects of ozone and catalyst dosages were investigated, and the changes in the organics, biotoxicity, and biodegradability of wastewater were analyzed using three-dimensional fluorescence spectroscopy, BOD5/COD, and specific oxygen uptake rate (SOUR). Energy consumption was also calculated. The removal efficiencies (at a flow rate of 1.0 m(3)/h, ozone dosage of 179 g/h, and catalyst dosage of 40 kg/m(3)) of COD and BOD5 were 61.77% and 16.98%, respectively, after 30 days of continuous operation. Toxic refractory macromolecular organic pollutants were transformed into small, biodegradable substances. BOD5/COD increased from 0.16 to 0.35, and SOUR decreased from 2.8460 to 2.3180 mgO(2)/(gMLSS.h). The operating cost was 1.24 kWh/kg COD. Catalytic ozonation as a pretreatment technology markedly improved biodegradability, reduced biotoxicity, and looks to be promising for treating fixed-bed coal gasification wastewater. (C) 2020 Published by Elsevier B.V. on behalf of Institution of Chemical Engineers.

关键词： Fixed-bed coal gasification wastewater Pilot-scale Catalytic ozonation Fe-Mn/lava catalyst Pretreatment Biodegradability

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Characteristics and mechanism of adsorption of tartaric acid by carbide slag ascertained and applied to prepare a binder

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JOURNAL OF CLEANER PRODUCTION 2022年 337卷

作者： Qin, Yihe Zhang, Kai he, xuwen China Univ Min & Technol Beijing Peoples R China

Pelletization is essential for the reutilization of mineral fines, and binder plays a crucial role in preparing pellets that satisfy strength requirements. In this study, carbide slag (CS) was modified with tartaric acid (TA) using a novel binder (TACS) to produce red mud pellets. In this study, to investigate the effect of TACS on the compressive strength of the pellets and ascertain the mechanism of adsorption of TA onto CS during pelletizing and adsorption experiments, the changes in crystal structure, functional groups, and valence states of elements in a solution of CS in TA were analyzed, and density functional theory calculations and molecular dynamics simulations were performed. The target pellet strength was achieved by using TACS to prepare cold-pressed red mud pellets at a solid-to-liquid ratio of 0.20. The pH of the TA solution used to prepare the binder was 13.00. The maximum adsorption capacity of CS for TA, calculated using the Langmuir model, was 336.33 mg.g(-1). The TACS was analyzed using X-ray powder diffraction, Fourier-transform infrared spectroscopy, Brunauer-Emmett-Teller method, and X-ray photoelectron spectroscopy. The analysis results confirmed that the TACS mainly comprised Ca(OH)(2) and that the adsorption of TA onto CS involved complexation and hydrogen bonding. The complexing reactions of the carboxyl and hydroxyl groups of TA with the Ca ion were dominant. This study provides experimental and theoretical guidance for future studies on the preparation of composite binders.

关键词： Tartaric acid Carbide slag Adsorption Binder Density functional theory calculation Molecular dynamics simulation

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煤矿矿井水资源化利用技术创新

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煤炭科学技术 2023年第1期51卷 523-530页

作者：何绪文王绍州张学伟李福勤中国矿业大学(北京)化学与环境工程学院北京100083 河北工程大学能源与环境工程学院河北邯郸056038

国家对煤矿矿井水资源化利用高度重视,排放标准的不断提高和智慧矿山概念的不断深化,给矿井水处理技术提出了新的挑战。通过对矿井水地面处理技术与井下多级过滤处理技术的分析和比较,在总结了现有技术存在问题的基础上,提出了聚瓷膜超... 详细信息

国家对煤矿矿井水资源化利用高度重视,排放标准的不断提高和智慧矿山概念的不断深化,给矿井水处理技术提出了新的挑战。通过对矿井水地面处理技术与井下多级过滤处理技术的分析和比较,在总结了现有技术存在问题的基础上,提出了聚瓷膜超滤高效处理矿井水新技术。新技术相比于传统处理技术的多流程而言,因处理过程无需加药,可省去整个加药系统,故处理过程仅包括高压预处理与常压过滤处理2个主要单元,解决了矿井水传统处理方法必须加混凝剂的难题,可真正做到矿井水处理全过程无人值守,符合智慧矿山的要求。新技术具有流程短、占地小、无需加药、出水水质稳定、可自动运行等特点,同时还可实现模块化、智能化,可适用于不同场景下含高悬浮物矿井水的直接过滤,或作为矿井水苦咸水的预处理单元,其中特别适合用于矿井水的井下处理就地复用。其核心组件聚瓷膜更是具有强度高、通量大、超亲水疏油、耐受悬浮物(SS)质量浓度可达10 000 mg/L等优势。通过实验室试验和矿井水地面处理改造工程案例可知,在进水SS质量浓度为0~10 000 mg/L,聚瓷膜直滤后出水SS均能稳定达到1 mg/L以下,浊度稳定达到1 NTU以下,该技术在煤矿矿井水资源化利用方面有明显优势,可为将来的大规模工程化推广应用提供技术参考。

关键词：矿井水悬浮物聚瓷膜资源化利用煤矿

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A Three-Dimensional Cyclic Multiplication - Convolution Neural Network for Hyperspectral Image Classification 19

A Three-Dimensional Cyclic Multiplication - Convolution Neur...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Xiaorui Huang, Zihan Sheng, Zhongyin Xu, Zihan he, xuwen Lin, Qiaoling Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan China

ISBN: (纸本)9798350346558

Hyperspectral images have a large amount of spectral and spatial information, and play a significant role in remote sensing. However, the traditional image classification techniques are no longer applicable to the developing hyperspectral remote sensing images, and how to reasonably utilize the rich feature information of hyperspectral images to obtain high-accuracy classification results has become one of the most important research topics at present. This paper proposes a new 3D convolutional neural network to solve the problem of feature wastage in hyperspectral images. The framework cycles the convolutional kernel size, and the number of convolutional kernels increases in a multiplicative relationship for hyperspectral image classification (CM-CNN). Compared with existing neural network frameworks on different datasets, CMCNN achieves the highest Kappa coefficient and confusion matrix accuracy, which are stable at above 95%. From the classification results, there are almost no obvious classification errors. © 2022 IEEE.

关键词： Image classification

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